We introduce a benchmarking framework Dyport for evaluating biomedical hypothesis generation (HG) and literature based discovery (LBD) systems. The evaluation of HG and LBD is still one of the major problems of these systems, especially when it comes to fully automated large-scale general purpose systems. For these, a massive assessment (that is normal in the machine learning and general AI domains) is often infeasible. One traditional evaluation approach is to make a system “rediscover” some of the landmark findings. However, this approach does not scale. Another traditional approach is to automatically discover some information in biomedical texts, train the system on historical data and test it on that "future" information. While this approach does scale well, the reliability and biomedical importance of the extracted test set are far from being illuminating.
Utilizing curated datasets, Dyport tests HG/LBD systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypothesis accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community.
Dyport is available at https://github.com/IlyaTyagin/Dyport
Paper: https://arxiv.org/pdf/2312.03303.pdf
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